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Paper Citation Record · LEDGER

One STEP at a time: Language Agents are Stepwise Planners

As of 18 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2411.08432.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.08432 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:40:44.397579Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved47
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e81531c2-2b50-472b-937b-d204faa0bcb0 · outbound

This paper cites online" 'onlinestring :=.

One STEP at a time: Language Agents are Stepwise Planners online" 'onlinestring :=

Reference 1

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source=arxiv_source observed=2026-08-12T21:40:44.156468Z digest=sha256:e4f8700637529639e92122b9e4192ce68b0ee36086247e87e3bb0ad735d6199f

Observation dc0be3d3-2062-4653-9f6b-5b5dfa6b1d71 · outbound

This paper cites write newline.

One STEP at a time: Language Agents are Stepwise Planners write newline

Reference 2

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source=arxiv_source observed=2026-08-12T21:40:44.162197Z digest=sha256:5ca0ae602717176cd4ee77a45d3370c961cc01c9bb4881d8f47b82c29c9a18e4

Observation 3b4364ad-029d-4e37-9b8c-a39c7a437977 · outbound

This paper cites Do As I Can, Not As I Say: Grounding Language in Robotic Affordances.

One STEP at a time: Language Agents are Stepwise Planners Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Reference 3

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source=arxiv_source observed=2026-08-12T21:40:44.168045Z digest=sha256:33e363b0f6feef97073f5cbc7be74146627edba7e21b3425d12da1256626d2ae

Observation 8db16dd8-b764-479a-b564-667bb2b11afd · outbound

This paper cites Graph Constrained Reinforcement Learning for Natural Language Action Spaces.

One STEP at a time: Language Agents are Stepwise Planners Graph Constrained Reinforcement Learning for Natural Language Action Spaces

Reference 4

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source=arxiv_source observed=2026-08-12T21:40:44.173789Z digest=sha256:38297a77840f1fe532dbdef535583411d258e91058207f408a120749d8ccbde3

Observation 9e3148ff-8b87-4a44-9778-69c3d5757be0 · outbound

This paper cites an unresolved cited work.

One STEP at a time: Language Agents are Stepwise Planners Unresolved cited work

Reference 5

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source=arxiv_source observed=2026-08-12T21:40:44.179422Z digest=sha256:172159d3117222840a51f0ca5279ecac93d30a53465984d992192595e1526e35

Observation e9cad111-cb98-46b8-a842-0b9f166ea841 · outbound

This paper cites Deep reinforcement learning from human preferences.

One STEP at a time: Language Agents are Stepwise Planners Deep reinforcement learning from human preferences

Reference 6

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source=arxiv_source observed=2026-08-12T21:40:44.184594Z digest=sha256:2c5fd808ab9826b6898470b5f780b266dddc392b5d615e55de1ede3570ecb06f

Observation ab938670-212b-4959-89f2-8edb77f65705 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

One STEP at a time: Language Agents are Stepwise Planners Training Verifiers to Solve Math Word Problems

Reference 7

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source=arxiv_source observed=2026-08-12T21:40:44.189854Z digest=sha256:4c4b7642a8a5ba60c22e48805f739bef13ecf0f61f4166c4d56007522b1a1e75

Observation eef30407-c33b-4e50-b113-10ce5315187b · outbound

This paper cites Dynamic Planning with a LLM.

One STEP at a time: Language Agents are Stepwise Planners Dynamic Planning with a LLM

Reference 8

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source=arxiv_source observed=2026-08-12T21:40:44.195906Z digest=sha256:033870f571a89a5f54d3c95c39d0944e8ea9b2ef131965a214d10871f6ea892a

Observation c15fcc6f-a954-4f49-be34-197003fc33d1 · outbound

This paper cites Language Models can be Logical Solvers.

One STEP at a time: Language Agents are Stepwise Planners Language Models can be Logical Solvers

Reference 9

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source=arxiv_source observed=2026-08-12T21:40:44.201822Z digest=sha256:ddee44095e65f0afc3fd653343d16386951f771b1f39a18f4616e00eab24d9f0

Observation ede06ed7-417b-4f79-aa1d-3e0eddf9b1c8 · outbound

This paper cites AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents.

One STEP at a time: Language Agents are Stepwise Planners AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents

Reference 10

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source=arxiv_source observed=2026-08-12T21:40:44.207200Z digest=sha256:3f32d2b636c079c72e8e5614514d0051fd19bc3ed868290b0ab4cd8122ab65a4

Observation b1b3899a-7999-43dc-9ee4-18ecbd7a06a6 · outbound

This paper cites Introduction to Reinforcement Learning.

One STEP at a time: Language Agents are Stepwise Planners Introduction to Reinforcement Learning

Reference 11

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source=arxiv_source observed=2026-08-12T21:40:44.213014Z digest=sha256:1db82c78b4b63bb747f98a5a071b9330c8cd223b40f376631fbb5c56cd4f420c

Observation 0c47e3b1-09c6-4d56-973b-b7eae6e3e709 · outbound

This paper cites CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing.

One STEP at a time: Language Agents are Stepwise Planners CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing

Reference 12

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source=arxiv_source observed=2026-08-12T21:40:44.218766Z digest=sha256:f99ab6a8327e972c8633e747619c528f1a58385fc9833bbed2d663f0edb601c3

Observation dc86d269-64e0-4022-948e-c17f395a0fc7 · outbound

This paper cites ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving.

One STEP at a time: Language Agents are Stepwise Planners ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving

Reference 13

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source=arxiv_source observed=2026-08-12T21:40:44.223782Z digest=sha256:78dba1586a01aa04e58f8654546e0f067609f9af27df6d7fd36f1ca929ae9107

Observation 0e179922-b4aa-4198-bf43-b886f663d2d9 · outbound

This paper cites Reasoning with Language Model is Planning with World Model.

One STEP at a time: Language Agents are Stepwise Planners Reasoning with Language Model is Planning with World Model

Reference 14

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source=arxiv_source observed=2026-08-12T21:40:44.229112Z digest=sha256:cb0dbbee342112054d0a62cf0880967da795b7366e43177bb0f0b62b9f791ff0

Observation c8a51292-2cc4-4d1f-b344-e87a0b9d9c0a · outbound

This paper cites Deep Reinforcement Learning with a Natural Language Action Space.

One STEP at a time: Language Agents are Stepwise Planners Deep Reinforcement Learning with a Natural Language Action Space

Reference 15

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source=arxiv_source observed=2026-08-12T21:40:44.234285Z digest=sha256:a0528ab9f84decff8810f34ae273e7c41c640b98dcf1f113f290aaf207d88a15

Observation 4e6b679c-9bf2-4237-91cb-0458a6ff6149 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

One STEP at a time: Language Agents are Stepwise Planners Measuring Mathematical Problem Solving With the MATH Dataset

Reference 16

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source=arxiv_source observed=2026-08-12T21:40:44.239363Z digest=sha256:1424de61286b9ee2763018ada86245ff0d41883bbef9a88a11be6bb90b7e4be9

Observation 0b64d0f1-bb37-4b87-98f8-cd1034785263 · outbound

This paper cites Understanding the planning of LLM agents: A survey.

One STEP at a time: Language Agents are Stepwise Planners Understanding the planning of LLM agents: A survey

Reference 17

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source=arxiv_source observed=2026-08-12T21:40:44.244293Z digest=sha256:61f9b32aac0075d9c91b4d127420529f07de93b800d240f2b3f89db270c7edc7

Observation 33561b0d-687e-402d-bb80-e94025a03255 · outbound

This paper cites SWE-bench: Can Language Models Resolve Real-World GitHub Issues?.

One STEP at a time: Language Agents are Stepwise Planners SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Reference 18

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source=arxiv_source observed=2026-08-12T21:40:44.249245Z digest=sha256:1fc0097ec4c1162c27086c9552ea3584cb2f79574f7537740cfd0de3303d90bd

Observation a0d68c15-a524-4118-833a-068d68515176 · outbound

This paper cites LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks.

One STEP at a time: Language Agents are Stepwise Planners LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks

Reference 19

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source=arxiv_source observed=2026-08-12T21:40:44.254422Z digest=sha256:3f69fa8e66c3e9bb43e094df4ecd0e28bdf7c4d93d6f1c830b3c0cfec068990c

Observation e0cd36ec-ddaf-4160-b8e6-8573645b592e · outbound

This paper cites Think Before You Act: Decision Transformers with Working Memory.

One STEP at a time: Language Agents are Stepwise Planners Think Before You Act: Decision Transformers with Working Memory

Reference 20

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source=arxiv_source observed=2026-08-12T21:40:44.259692Z digest=sha256:557086d8c8b509685dad3789a781be52b84fe8fe8ad9689846456a18e448edc7

Observation 23b63b18-4bd3-43c8-9cb2-902cd3b4c276 · outbound

This paper cites Large Language Models are Zero-Shot Reasoners.

One STEP at a time: Language Agents are Stepwise Planners Large Language Models are Zero-Shot Reasoners

Reference 21

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source=arxiv_source observed=2026-08-12T21:40:44.264915Z digest=sha256:e5c8f63e3fbb16800d77166f867bf35662e865e41e7ebf1b16e38a02658fe6b1

Observation 3efc3b9e-adfd-4b91-acb8-edce35e6364e · outbound

This paper cites SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks.

One STEP at a time: Language Agents are Stepwise Planners SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks

Reference 22

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source=arxiv_source observed=2026-08-12T21:40:44.269743Z digest=sha256:8b88bc73a0986f0240669f848d8e3a353a7cbf4ea7738b85a230259cd2c964dd

Observation 3a42bacc-ef36-4d31-9618-7a4c264d6293 · outbound

This paper cites LLM+P: Empowering Large Language Models with Optimal Planning Proficiency.

One STEP at a time: Language Agents are Stepwise Planners LLM+P: Empowering Large Language Models with Optimal Planning Proficiency

Reference 23

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source=arxiv_source observed=2026-08-12T21:40:44.274888Z digest=sha256:dc32d3e955186bc7170ffa4cc8146c6bdd6bd7695fa1be0063645787ee76f1af

Observation 0a1bd1dc-468e-4f5c-a6d3-50aef5b95ab7 · outbound

This paper cites Self-Refine: Iterative Refinement with Self-Feedback.

One STEP at a time: Language Agents are Stepwise Planners Self-Refine: Iterative Refinement with Self-Feedback

Reference 24

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source=arxiv_source observed=2026-08-12T21:40:44.279795Z digest=sha256:9d544195cf5de9e20fb2d89da0fc3dc7d13069d0c976f2eed1378005c2fabe34

Observation ea34afc0-8619-48e0-a12c-05ae70636973 · outbound

This paper cites CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization.

One STEP at a time: Language Agents are Stepwise Planners CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization

Reference 25

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source=arxiv_source observed=2026-08-12T21:40:44.284722Z digest=sha256:d068944ab638d5b733a5ddda285c4ecb5e9b0035e828785534d7dd8861ba2a75

Observation 9270adb3-6f02-4815-bdf5-92584b9cc7e6 · outbound

This paper cites Training language models to follow instructions with human feedback.

One STEP at a time: Language Agents are Stepwise Planners Training language models to follow instructions with human feedback

Reference 26

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source=arxiv_source observed=2026-08-12T21:40:44.289829Z digest=sha256:95da827b2d08c9576b6e48e4c02974d09080bad54095dd628167bd44c63790ea

Observation ff6a8a44-26e4-4c2a-9983-e857be26117b · outbound

This paper cites Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought.

One STEP at a time: Language Agents are Stepwise Planners Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought

Reference 27

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source=arxiv_source observed=2026-08-12T21:40:44.295028Z digest=sha256:43df19effdcce1b63f56d861761defb259d1763affd7227001260af0af1cb66b

Observation 72089e18-3836-468a-bc83-891e905e3bea · outbound

This paper cites Confident Adaptive Language Modeling.

One STEP at a time: Language Agents are Stepwise Planners Confident Adaptive Language Modeling

Reference 28

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source=arxiv_source observed=2026-08-12T21:40:44.300064Z digest=sha256:8dfca61571aa5e53bd415717877d617a6b375c32d822954a008fc27c08905385

Observation fc7407f5-5003-4383-be73-69d283a5b123 · outbound

This paper cites HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face.

One STEP at a time: Language Agents are Stepwise Planners HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face

Reference 29

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source=arxiv_source observed=2026-08-12T21:40:44.305297Z digest=sha256:dc7e302758082625707e09177a1ed92d80a93c1cdfe8dfa85bbe56bdbc723cc7

Observation 89e10d9e-e70c-46e0-986a-9265ffb9db25 · outbound

This paper cites Reflexion: Language Agents with Verbal Reinforcement Learning.

One STEP at a time: Language Agents are Stepwise Planners Reflexion: Language Agents with Verbal Reinforcement Learning

Reference 30

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source=arxiv_source observed=2026-08-12T21:40:44.310154Z digest=sha256:1cacf49f8bb87d69d074c217cc1f0b642f0483a4a3b09a8a1d3a3a6ffcd32753

Observation 67eac9c9-33a1-46b1-ba6c-a7dbbe59c627 · outbound

This paper cites ALFWorld: Aligning Text and Embodied Environments for Interactive Learning.

One STEP at a time: Language Agents are Stepwise Planners ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

Reference 31

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source=arxiv_source observed=2026-08-12T21:40:44.315299Z digest=sha256:2499fa42d9d2e863c58c2ea9214ce26c9ad6200da4b7771a3e267a6a424f8fa9

Observation 1c33cd22-5d1c-42d1-8f1a-a7961efdb4ee · outbound

This paper cites ProgPrompt: Generating Situated Robot Task Plans using Large Language Models.

One STEP at a time: Language Agents are Stepwise Planners ProgPrompt: Generating Situated Robot Task Plans using Large Language Models

Reference 32

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source=arxiv_source observed=2026-08-12T21:40:44.320186Z digest=sha256:5b3dc7f6349224b40d86faf23d01c5d325b0aaa29d5605f0a06cde72808a1a51

Observation 68fbad43-9b26-4e67-ab9f-94a89aa670a5 · outbound

This paper cites Cognitive Architectures for Language Agents.

One STEP at a time: Language Agents are Stepwise Planners Cognitive Architectures for Language Agents

Reference 33

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source=arxiv_source observed=2026-08-12T21:40:44.325147Z digest=sha256:891d6b56f7e3c3ce912c5a2612b8411800698fd815f8d81e0c0a3e0f32f34c29

Observation 426b9668-626a-4ffe-a8fd-26de1ae858bc · outbound

This paper cites ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language.

One STEP at a time: Language Agents are Stepwise Planners ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language

Reference 34

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source=arxiv_source observed=2026-08-12T21:40:44.330129Z digest=sha256:4a7a7c37e90d19c0089959c79099672ac1d6313653e141719143f0040e4553df

Observation 9eb97eb7-a5de-41db-8a88-c0eb63cba666 · outbound

This paper cites Can Large Language Models Really Improve by Self-critiquing Their Own Plans?.

One STEP at a time: Language Agents are Stepwise Planners Can Large Language Models Really Improve by Self-critiquing Their Own Plans?

Reference 35

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source=arxiv_source observed=2026-08-12T21:40:44.335667Z digest=sha256:5d4507656295249c321e016f607b0bc7e328655c9f05de6cf63c43ea0dfc349b

Observation 003b80b6-acb1-4381-b73e-15cb0963c600 · outbound

This paper cites Voyager: An Open-Ended Embodied Agent with Large Language Models.

One STEP at a time: Language Agents are Stepwise Planners Voyager: An Open-Ended Embodied Agent with Large Language Models

Reference 36

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source=arxiv_source observed=2026-08-12T21:40:44.340700Z digest=sha256:6f5ba94dcb556184f568642f65350b290db9a7837b310daff78087437fc782fb

Observation 64266c7d-ba6c-4812-a3a4-b53c8a8710db · outbound

This paper cites Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models.

One STEP at a time: Language Agents are Stepwise Planners Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

Reference 37

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source=arxiv_source observed=2026-08-12T21:40:44.345538Z digest=sha256:0d4d3527e9dd5efc4af2223afbe784095e777ec4a0038f4321eabce291dbcd27

Observation 9784c780-2fe2-49a7-9d25-5a54a8455e23 · outbound

This paper cites ScienceWorld: Is your Agent Smarter than a 5th Grader?.

One STEP at a time: Language Agents are Stepwise Planners ScienceWorld: Is your Agent Smarter than a 5th Grader?

Reference 38

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.350267Z digest=sha256:35dc489cad6467a23c8625b446af86a3d829f5173d7bc866f24c1a7aaac6ceba

Observation ee0d92dd-c840-4662-b71f-a92186de6461 · outbound

This paper cites WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents.

One STEP at a time: Language Agents are Stepwise Planners WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-12T21:40:44.355321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.355321Z digest=sha256:ee9f97caf7f490fd133be844e34c01f3e7677e58d4d7dfb1691e172d366f7fed

Observation 17b58234-acf6-422a-9019-229802c725ab · outbound

This paper cites Tree of Thoughts: Deliberate Problem Solving with Large Language Models.

One STEP at a time: Language Agents are Stepwise Planners Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-12T21:40:44.360049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.360049Z digest=sha256:9cf992668ad45e4c43020134fd9bc7384cd95bfaaedb0ee94887a01f1d79897c

Observation 0b7b6502-e76f-42ee-aa05-2045e5811125 · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

One STEP at a time: Language Agents are Stepwise Planners ReAct: Synergizing Reasoning and Acting in Language Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-12T21:40:44.365078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.365078Z digest=sha256:86c3abc2c1f7219fe77071104a5b22aaf1512ab672c776dc3cad3db75f10a820

Observation d04d520c-b4ed-4c4c-ad58-19fd2359d3ce · outbound

This paper cites ExpeL: LLM Agents Are Experiential Learners.

One STEP at a time: Language Agents are Stepwise Planners ExpeL: LLM Agents Are Experiential Learners

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-12T21:40:44.370287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.370287Z digest=sha256:a200f9d5913e1337584cf1ba687e10b1b35c494425bf03e04a32dd3a6a35aa7d

Observation 624d1bc3-e9d0-4b40-b2aa-74a648f42db7 · outbound

This paper cites Large Language Models as Commonsense Knowledge for Large-Scale Task Planning.

One STEP at a time: Language Agents are Stepwise Planners Large Language Models as Commonsense Knowledge for Large-Scale Task Planning

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-12T21:40:44.376910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.376910Z digest=sha256:a28e9b96d6a437ba933663337e037ab5dd375d0ca3faf8f89a5a2ba947f1a56d

Observation 91dd0862-997b-436f-bf5b-eacb3178b99d · outbound

This paper cites Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models.

One STEP at a time: Language Agents are Stepwise Planners Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-12T21:40:44.382136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.382136Z digest=sha256:b9efcb49694725f130df4eb2739e5152c87560bf066dc218f7bce650e18f6827

Observation 4908903a-adbc-41aa-bba4-38a4237843f2 · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

One STEP at a time: Language Agents are Stepwise Planners Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-12T21:40:44.387517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.387517Z digest=sha256:7b46ab0f4e1ed4001169e5d9da0d4092ab42ed79e2ea987a36b69e85f12c2776

Observation 6e6966ec-0555-4833-b549-8a70bf9d2193 · outbound

This paper cites WebArena: A Realistic Web Environment for Building Autonomous Agents.

One STEP at a time: Language Agents are Stepwise Planners WebArena: A Realistic Web Environment for Building Autonomous Agents

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-12T21:40:44.392686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.392686Z digest=sha256:b24cda5ab1d208890b2b6d4da5ba8cdda27c72409026592aa88d850188f1d347

Observation bb39c9d1-1a04-4101-bd52-7e2bf7203d13 · outbound

This paper cites BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions.

One STEP at a time: Language Agents are Stepwise Planners BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-12T21:40:44.397579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T21:40:44.397579Z digest=sha256:0881ad2ecc89f7b095b4f3b85fe72fe7a8cd4486e8e822b4c38e33c6739beef1

Pith citing papers

No inbound Pith citation observations are available.